| 0.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 16 | | adverbTagCount | 5 | | adverbTags | | 0 | "he replied casually [casually]" | | 1 | "Rory finally asked [finally]" | | 2 | "she asked finally [finally]" | | 3 | "he replied sharply [sharply]" | | 4 | "she said instead [instead]" |
| | dialogueSentences | 43 | | tagDensity | 0.372 | | leniency | 0.744 | | rawRatio | 0.313 | | effectiveRatio | 0.233 | |
| 62.24% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 927 | | totalAiIsmAdverbs | 7 | | found | | 0 | | | 1 | | | 2 | | | 3 | | | 4 | | adverb | "barely above a whisper" | | count | 1 |
|
| | highlights | | 0 | "sharply" | | 1 | "slightly" | | 2 | "lazily" | | 3 | "lightly" | | 4 | "barely above a whisper" |
| |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 2.91% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 927 | | totalAiIsms | 18 | | found | | | highlights | | 0 | "echoed" | | 1 | "unreadable" | | 2 | "electric" | | 3 | "intensity" | | 4 | "reminder" | | 5 | "silence" | | 6 | "scanned" | | 7 | "chaotic" | | 8 | "charm" | | 9 | "tracing" | | 10 | "eyebrow" | | 11 | "whisper" | | 12 | "tension" | | 13 | "weight" | | 14 | "warmth" | | 15 | "unspoken" | | 16 | "flicker" |
| |
| 66.67% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 2 | | maxInWindow | 2 | | found | | 0 | | label | "weight of words/silence" | | count | 1 |
| | 1 | | label | "hung in the air" | | count | 1 |
|
| | highlights | | 0 | "the weight of unspoken words" | | 1 | "hung in the air" |
| |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 56 | | matches | (empty) | |
| 91.84% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 2 | | narrationSentences | 56 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 83 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 27 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 920 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 14 | | unquotedAttributions | 0 | | matches | (empty) | |
| 33.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 27 | | wordCount | 645 | | uniqueNames | 5 | | maxNameDensity | 2.02 | | worstName | "Rory" | | maxWindowNameDensity | 4 | | worstWindowName | "Rory" | | discoveredNames | | Eva | 2 | | Carter | 1 | | Rory | 13 | | Moreau | 1 | | Lucien | 10 |
| | persons | | 0 | "Eva" | | 1 | "Carter" | | 2 | "Rory" | | 3 | "Moreau" | | 4 | "Lucien" |
| | places | (empty) | | globalScore | 0.492 | | windowScore | 0.333 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 48 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 920 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 83 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 48 | | mean | 19.17 | | std | 13.33 | | cv | 0.695 | | sampleLengths | | 0 | 58 | | 1 | 54 | | 2 | 33 | | 3 | 14 | | 4 | 19 | | 5 | 13 | | 6 | 50 | | 7 | 20 | | 8 | 13 | | 9 | 8 | | 10 | 11 | | 11 | 32 | | 12 | 31 | | 13 | 19 | | 14 | 14 | | 15 | 18 | | 16 | 24 | | 17 | 22 | | 18 | 13 | | 19 | 5 | | 20 | 33 | | 21 | 8 | | 22 | 12 | | 23 | 5 | | 24 | 8 | | 25 | 20 | | 26 | 19 | | 27 | 7 | | 28 | 6 | | 29 | 11 | | 30 | 4 | | 31 | 11 | | 32 | 31 | | 33 | 8 | | 34 | 21 | | 35 | 27 | | 36 | 18 | | 37 | 4 | | 38 | 6 | | 39 | 16 | | 40 | 12 | | 41 | 12 | | 42 | 38 | | 43 | 15 | | 44 | 17 | | 45 | 18 | | 46 | 10 | | 47 | 52 |
| |
| 99.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 56 | | matches | | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 107 | | matches | | |
| 39.59% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 5 | | semicolonCount | 0 | | flaggedSentences | 3 | | totalSentences | 83 | | ratio | 0.036 | | matches | | 0 | "When she unlocked the three deadbolts and swung the door open, she was met with a pair of eyes—one amber, the other black." | | 1 | "\"Lucien,\" she breathed, a mixture of surprise and something else—something electric—flickering in her voice." | | 2 | "The faint scent of his cologne—something dark and woody—filled the room." |
| |
| 71.79% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 652 | | adjectiveStacks | 1 | | stackExamples | | 0 | "small crescent-shaped scar" |
| | adverbCount | 36 | | adverbRatio | 0.05521472392638037 | | lyAdverbCount | 20 | | lyAdverbRatio | 0.03067484662576687 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 83 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 83 | | mean | 11.08 | | std | 5.96 | | cv | 0.538 | | sampleLengths | | 0 | 16 | | 1 | 16 | | 2 | 12 | | 3 | 14 | | 4 | 21 | | 5 | 13 | | 6 | 20 | | 7 | 23 | | 8 | 10 | | 9 | 14 | | 10 | 15 | | 11 | 4 | | 12 | 13 | | 13 | 21 | | 14 | 11 | | 15 | 18 | | 16 | 20 | | 17 | 13 | | 18 | 2 | | 19 | 6 | | 20 | 7 | | 21 | 4 | | 22 | 19 | | 23 | 13 | | 24 | 10 | | 25 | 21 | | 26 | 13 | | 27 | 6 | | 28 | 14 | | 29 | 7 | | 30 | 11 | | 31 | 13 | | 32 | 11 | | 33 | 15 | | 34 | 7 | | 35 | 7 | | 36 | 6 | | 37 | 5 | | 38 | 19 | | 39 | 14 | | 40 | 8 | | 41 | 12 | | 42 | 4 | | 43 | 1 | | 44 | 8 | | 45 | 6 | | 46 | 14 | | 47 | 19 | | 48 | 7 | | 49 | 4 |
| |
| 83.13% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 1 | | diversityRatio | 0.5060240963855421 | | totalSentences | 83 | | uniqueOpeners | 42 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 64.44% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 21 | | totalSentences | 54 | | matches | | 0 | "Her eyes, bright blue as" | | 1 | "She wasn’t expecting anyone, and" | | 2 | "She crossed the room in" | | 3 | "she breathed, a mixture of" | | 4 | "he replied casually, but his" | | 5 | "she muttered, stepping aside to" | | 6 | "He moved into the flat" | | 7 | "She shook her head lightly," | | 8 | "she snapped, moving past him" | | 9 | "She dumped them unceremoniously onto" | | 10 | "He set his ivory-handled cane" | | 11 | "She shook her head, a" | | 12 | "She sighed, leaning back in" | | 13 | "It was an old habit," | | 14 | "she asked finally" | | 15 | "she said, her voice barely" | | 16 | "he replied sharply, cutting through" | | 17 | "she scoffed, standing and pacing" | | 18 | "He flinched, the mask slipping" | | 19 | "She turned away." |
| | ratio | 0.389 | |
| 0.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 52 | | totalSentences | 54 | | matches | | 0 | "The sharp knock echoed through" | | 1 | "Aurora Carter, or Rory as" | | 2 | "Her eyes, bright blue as" | | 3 | "She wasn’t expecting anyone, and" | | 4 | "Ptolemy, the tabby cat, stretched" | | 5 | "Rory pushed back her chair," | | 6 | "She crossed the room in" | | 7 | "Lucien Moreau stood there, his" | | 8 | "she breathed, a mixture of" | | 9 | "he replied casually, but his" | | 10 | "she muttered, stepping aside to" | | 11 | "He moved into the flat" | | 12 | "The faint scent of his" | | 13 | "Rory shut the door behind" | | 14 | "She shook her head lightly," | | 15 | "Lucien's lips twisted into a" | | 16 | "she snapped, moving past him" | | 17 | "She dumped them unceremoniously onto" | | 18 | "Lucien said, lowering himself into" | | 19 | "He set his ivory-handled cane" |
| | ratio | 0.963 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 54 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 26 | | technicalSentenceCount | 0 | | matches | (empty) | |
| 0.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 16 | | uselessAdditionCount | 4 | | matches | | 0 | "he replied casually, but his eyes held an intensity that belied his nonchalance" | | 1 | "Rory finally asked, her voice softer than before" | | 2 | "she said, her voice barely above a whisper" | | 3 | "Lucien replied, the faint smile returning to his lips" |
| |
| 80.23% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 11 | | fancyCount | 3 | | fancyTags | | 0 | "she breathed (breathe)" | | 1 | "she muttered (mutter)" | | 2 | "she snapped (snap)" |
| | dialogueSentences | 43 | | tagDensity | 0.256 | | leniency | 0.512 | | rawRatio | 0.273 | | effectiveRatio | 0.14 | |